Computer Science Work Repository

This repository showcases a collection of projects and assignments spanning core areas of computer science, from low-level hardware interactions to high-level algorithmic problem-solving, machine learning, and artificial intelligence. It reflects a strong foundation in computer systems, algorithms, and data-driven computing.

Repository Overview

This repository is organized into eight main areas:

1. Assembly Language

   • Low-level programming using assembly language, focusing on direct interaction with hardware components.
   • Demonstrates understanding of CPU architecture, memory management, and instruction sets.

2. Computer Architecture

   • Explores how computers are structured and operate at the hardware level.
   • Includes topics such as logic gates, processor design, memory hierarchy, and performance optimization.

3. Data Structures and Algorithms

   • Contains implementations of fundamental data structures (e.g., arrays, linked lists, trees, graphs) and algorithms (e.g., sorting, searching, dynamic programming).
   • Emphasizes algorithm design, complexity analysis, and real-world application.

4. Numerical Methods

   • Focuses on solving mathematical problems computationally, using techniques such as interpolation, root finding, and solving systems of equations.
   • Includes iterative methods, matrix operations, and polynomial manipulation.

5. Systems Programming

   • Implementations of basic C programs which touch on lower level system concepts such as socket programming, multithreading, and file processing.
   • Helped gain insight into my first lower level language and shows application of data structures and algorithms.

6. GPU Computing

   • Practical experience with massively parallel programming using CUDA C, executed on the NCSA Delta supercomputer node.
   • Custom CUDA kernels for:
     • Matrix Multiplication: Multiple versions optimized for performance benchmarking.
     • Vector Multiplication: High-performance implementations.
     • Convolution Operations: Designed to measure and compare performance under varying conditions.
   • Focuses on performance measurements and optimizations for parallel computation.

7. Database Systems

   • Focused on writing SQL queries and exploring database features.
   • Includes:
     • SQL Querying: Writing complex queries using JOINs, subqueries, window functions, and aggregations.
     • Stored Procedures & Triggers: Automating tasks and enforcing business rules within the database.
     • Schema Design & Normalization: Structuring relational databases for efficiency and scalability.
     • Hands-on Experience: Working with MySQL services.

8. Machine Learning & AI

   • Practical experience with machine learning techniques and theory.
   • Supervised Learning: Implementations of regression, classification, and neural networks.
   • Unsupervised Learning: Clustering techniques and dimensionality reduction.

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The Learning Journey: From Hardware to Intelligent Systems

This repository follows a progression from understanding low-level hardware interactions in Assembly Language and Computer Architecture to mastering high-level algorithmic thinking in Data Structures and Algorithms. By working through these areas, I have built a robust foundation in both computer system fundamentals and the design of efficient software solutions.

• Assembly Language gives insight into how software interacts directly with hardware, allowing for efficient resource management.
• Computer Architecture bridges the gap between software and hardware, providing a deeper understanding of how code executes on real machines.
• Data Structures and Algorithms take the lessons from lower levels and apply them to build scalable and optimized solutions for complex computational problems.
• Numerical Methods showcases the application of computational techniques to solve scientific and engineering problems.
• Systems Programming exhibits practical usage of a lower level programming language and systems programming concepts.
• GPU Computing demonstrates the power of parallel processing to accelerate data-intensive computations and highlights the importance of performance optimization in modern computing.
• Database Systems focuses on organizing, querying, and optimizing large-scale data storage solutions.
• AI & ML Harnessing data-driven intelligence for predictive modeling and decision-making.

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How to Navigate

Each directory corresponds to one of the eight focus areas. Inside each directory, you'll find code implementations and detailed explanations.

• AssemblyLanguage/: Contains assembly programs demonstrating fundamental hardware interactions.
• ComputerArchitecture/: Projects exploring the design and performance of computer systems.
• DataStructuresAlgorithms/: Implementations of common data structures and algorithms.
• NumericalMethods/: Numerical computation projects tackling mathematical problems.
• SystemsProgramming/: C programs showcases basic lower level system concepts such as multithreading, socket programming, and text processing.
• GPUComputing/: CUDA C programs focusing on parallel computing techniques, with performance measurements and custom kernel implementations.
• DatabaseSystems/: SQL scripts demonstrating query writing and database design.
• MachineLearningAI/: Machine learning models, deep learning experiments, and AI algorithms.